In recent years, stock prices have been predicted in various forms such as technical analysis and fundamental analysis using time series data and financial indicators, and text analysis using news information. In particular, some use text mining to predict whether stock prices will rise or fall from text information, but useful text information does not appear frequently on all stocks. Considering the increase in algorithmic trading using technical analysis, analysis that relies solely on textual information is not appropriate because it does not take into account the impact of such trading. Therefore, in this study, first, stocks that are easily affected by technical analysis were ranked by using machine learning to raise and lower stock prices using indicators that are often used in technical analysis techniques. Moreover, when the analysis did not go well, we analyzed what kind of events occurred and investigated how technical analysis affects the stock price.

In order to encourage individual asset flow into the Japanese market through long-term investments, it is important to evaluate stock values of companies because stock prices of companies are determined not only by internal values, which are independent of other companies, but also by market fundamentalism. However, there are few studies conducted in this area in the machine learning community, while there are many studies about prediction of stock price trends. These studies use a single factor approach (such as textual or numerical) and focus on internal values only. We propose a model where we combine two major financial approaches to evaluate stock values: technical analysis and fundamental analysis. The technical analysis is conducted using Long-Short Term Memory and technical indexes as input data. On the other hand, the fundamental analysis is conducted transversely and relatively by creating a program which can retrieve data on financial statements of all listed companies in Japan and put them into a database. From the experiments, compared to single technical analysis proposed model’s accuracy in classification was 11.92% more accurate and the relative error of regression was 3.77% smaller on average. In addition, compared to single factor approaches the accuracy in classification was 6.16% more accurate and the relative error of regression was 3.22% smaller on average. The proposed model has the potential to be combined with other prediction methods, such as textual approaches or even traditional financial approaches, which would improve accuracy and increase practicality of this model.